CoStruction: Conjoint radiance field optimization for urban scene reconStruction with limited image overlap (2501.03932v1)

Abstract: Reconstructing the surrounding surface geometry from recorded driving sequences poses a significant challenge due to the limited image overlap and complex topology of urban environments. SoTA neural implicit surface reconstruction methods often struggle in such setting, either failing due to small vision overlap or exhibiting suboptimal performance in accurately reconstructing both the surface and fine structures. To address these limitations, we introduce CoStruction, a novel hybrid implicit surface reconstruction method tailored for large driving sequences with limited camera overlap. CoStruction leverages cross-representation uncertainty estimation to filter out ambiguous geometry caused by limited observations. Our method performs joint optimization of both radiance fields in addition to guided sampling achieving accurate reconstruction of large areas along with fine structures in complex urban scenarios. Extensive evaluation on major driving datasets demonstrates the superiority of our approach in reconstructing large driving sequences with limited image overlap, outperforming concurrent SoTA methods.

• The paper introduces CoStruction, a novel hybrid method combining radiance fields and SDFs for accurate urban scene reconstruction with limited image overlap.
• CoStruction employs techniques like Guided Ray Sampling and Cross-Representation Uncertainty Estimation to enhance geometric precision and filter ambiguous data.
• Extensive experiments show CoStruction outperforms state-of-the-art methods on autonomous driving datasets, achieving improved accuracy in large urban scenes and fine details.

Overview of "CoStruction: Conjoint Radiance Field Optimization for Urban Scene Reconstruction with Limited Image Overlap"

The paper entitled "CoStruction: Conjoint Radiance Field Optimization for Urban Scene Reconstruction with Limited Image Overlap" introduces a novel hybrid methodology designed to address the complexities inherent in reconstructing urban environments with sparse photographic data. This research significantly focuses on enhancing the mapping and modeling accuracy of 3D urban scenes, especially under challenging conditions where image overlap is limited—a scenario often encountered in autonomous driving contexts.

Methodological Innovations

CoStruction combines strengths from both volumetric radiance fields and signed distance functions (SDFs) to facilitate precise geometrical reconstructions. Traditional approaches, such as Neural Radiance Fields (NeRF), often face challenges when extrapolating high-fidelity details in scenarios characterized by linear camera trajectories and insufficient multi-view image integration. The paper proposes a dual-representation strategy:

1. Guided Ray Sampling (GRS): By leveraging cross-representation uncertainty estimation, the method effectively filters out ambiguous geometry caused by limited observations, enhancing the clarity and accuracy of the resulting 3D models.
2. Cross-Representation Uncertainty Estimation: This component identifies and prioritizes geometrically consistent regions for precision-focused sampling. The dual-model setup (volumetric and SDF) ensures robustness by emphasizing areas where model predictions align closely with empirical data, inherently reducing noise and inaccuracies in the generated surfaces.
3. Adaptive Masked Eikonal Constrain: The application of these constraints facilitates precise surface regularization in stages, allowing for more flexible adaptability to scene-specific complexities during the model's training and refinement phases.

Experimental Validation

The paper demonstrates extensive experimental validations across four notable autonomous driving datasets: KITTI-360, Pandaset, Waymo Open Dataset, and nuScenes. CoStruction illustrates superior performance over existing state-of-the-art algorithms such as SCILLA and StreetSurf in terms of accuracy in reconstructing expansive urban landscapes as well as intricate structural details (e.g., fine wires, light posts).

Quantitatively, CoStruction achieves notable reductions in average point-to-mesh (P$\rightarrow$M) distances and enhancements in surface precision accuracy—evidenced by marked improvements registered across a variety of dataset sequences. These results underscore the model's efficacy in balancing computational efficiency with high-fidelity output.

Theoretical and Practical Implications

The theoretical underpinning of combining volumetric radiance fields with SDF models paves the path toward more accurate neural rendering solutions, particularly in outdoor autonomous scenarios where traditional geometry assumption constraints (e.g., texture-rich surfaces) fall short. In practice, the deployment of CoStruction in autonomous systems can potentially bridge the gap between real-time computational demands and the high precision required for safety-critical operations.

Future Directions

Looking forward, the paper's dual-model approach invites further exploration into adaptive complex hybrid models, potentially integrating more sophisticated uncertainty estimation methods that might harness additional modalities such as LiDAR. Furthermore, there is room to explore the scalability and adaptability of this methodology to other domains like augmented reality and smart city modeling where dynamic and unpredictable environments are the norms.

In conclusion, "CoStruction" offers a significant contribution to the domain of 3D urban scene reconstruction, providing a robust framework that adeptly handles the challenges posed by minimal image overlap—marking an advancement in the field of neural rendering and autonomous navigation technologies.